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Cohort Analysis

Checked
aj-geddes·vlatest·data·MIT

Analyze user groups ("cohorts") to identify behavioral trends and predict future actions based on shared experiences.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

Installs under a different name. Its SKILL.md names it Cohort Analysis, not the name it is listed under, so the command below uses that name.
  1. 1

    Run this in your project

    terminal
    $ npx skills add aj-geddes/useful-ai-prompts --skill "Cohort Analysis" -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

aj-geddes

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned aj-geddes/useful-ai-prompts at 3f5182cfd7.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
3f5182cfd7
Manifest
skills/cohort-analysis/SKILL.md
Scanned
3 files, 346 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

This skill enables AI agents to perform cohort analysis, a technique for tracking groups of users (cohorts) with shared characteristics over time. It allows you to identify behavioral trends, measure retention rates and churn, and predict future actions based on these shared experiences. The skill uses Python code internally to process user lifecycle data and generate insights about cohorts.

When to use it

  • Measuring user retention rates and identifying when users churn.
  • Analyzing customer lifetime value (LTV) and payback periods.
  • Comparing performance across different user acquisition channels or campaigns.
  • Understanding how product changes affect different user groups over time.
  • Tracking engagement patterns and identifying early warning signs of churn.

Key capabilities

  • Cohort Definition: Groups users based on shared characteristics like signup date, behavior, revenue, location, or demographics.
  • Retention Rate Calculation: Determines the percentage of a cohort that remains active over time.
  • Churn Rate Analysis: Identifies the percentage of a cohort that stops using a product or service.
  • Cohort Age Tracking: Monitors how cohorts degrade over time (e.g., monthly).
  • Data Visualization: Generates visualizations (using matplotlib and seaborn) to represent retention curves and other cohort-related metrics.

Example prompts

  • "Analyze users who signed up in January 2023 and show me their retention rate over the past six months."
  • "Compare the lifetime value of users acquired through Facebook ads versus those from organic search."
  • "Show me how product feature X impacted user engagement for cohorts after its release."

Tips & gotchas

  • The skill requires Python to be installed and accessible within the AI agent's environment.
  • The provided source code generates sample data; you will need to provide your own user lifecycle data for real-world analysis.
  • Understanding core concepts like "cohort size," "retention curve," and "churn rate" is helpful for interpreting results.